The Classification of Diseases changed in the 1850s due to William Farr’s work in medical topography, climatology, epidemiology and statistics. William Farr converted the miasma theory into a much more complex dendrogram of diseases, reclassifying the diseases and other medical conditions into yet a new classification system quite different from the dozen or so major systems that preceded his work. A decade later, with ten years available to reflect on these changes in the classification system or nosology of disease, William Aitken commented about William Farr’s changed, in retrospect to the others that were out there, but especially William Cullen. Farr’s methodology was much more complex that Cullen’s and the knowledge we had progressed to such an extent since Cullen’s system was in use in the 1790s that a number of fairly distinct and different ways to interpret diseases had been applied and since then observed, made use of and tested in the field by all the practitioners that followed.

Aitken himself said there were several ways we could go about breaking down diseases, comparing them, and designing these disease classification trees.

One of the most geographic forms of classifcation focused on the causes for disease was that of the geographer Canstatt discussed on another page. Composed about one to two decades earlier that Aitken’s work, and just a few years before Farr’s work, this nosology seemed to focus primarily upon individuals’ “local” or personal differences in physique, physiology, “temperament”, constitution, and heritage (that which today we’d consider pretty much equivalent to genetics), all in relation to where he/she was raised, where he/she was living, and the different kinds of natural events that occurred in these places related to climate, weather, seasonal and non-seasonal temperature changes, wind patterns, ecological variations over time, natural disasters or events, atmospheric disturbances, and planetary, solar and other astronomical changes and disturbances.

Nosology is essentially the precursor to today’s classifications systems out there for medicine. We learned to focus on a series of different approaches to understanding diseases. We tend to follow some of the more traditional methods relying upon organ systems such as heart, liver, intestines, etc. to classify certain diseases, our psychological and/or psychiatric interpretations of other conditions, our very strong knowledge base about the microorganismal causes for other, the venoms and toxins that produce yet more, and the fact that some diseases are very familiar and genetic linked. The ways in which these diseases are broken down into their smallest categories since ICD went into effect is an indicator of just how much Farr’s method of interpreting disease probably impacted the total system of health care and diagnosis, far more that similar very anatomical and physiological works produced in earlier times like those of William Cullen and even New York’s David Hosack, who summarized the previous nosologies into a single reference for comparisons.

There are of course numerous other examples of disease classification out there. Farr’s take on the value of classification systems as promoted by Linnaeus and Erasmus Darwin, the grandfather perhaps of the behavior and period in science fathered by Farr. The most recent transition from ICD 9 to ICD 10 demonstrates how much we have dissected down this way in which we tend to classify everything. With this new system, an outline perhaps makes better sense that the nomenclature and brands of ICD identifiers assigned to each order, genus, species, subspecies, variety, ad nauseum of disease (that’s R11.o, it used to be 787.02). Numbers are values that we can subconsciously assign rank values to and make use of in statistics; the letter reliant ICD 10 adds another step to the data reclass and recoding, making it difficult along the way by dividing down sections of data into smaller sections that don’t facilitate statistical analyses, but rather complicate them and make the worth more like two employees with jobs instead of one.

Fortunately, in terms of historical medical geography, we are fortunate in that the older methods work very well spatially. These newer methods are going to pose a major problem with the analysis and prediction of diseases, but will make for some very lucrative careers for those who cam best transpose ICD 10 data into effective integrative information of statistical value.